EEG source localization using a sparsity prior based on Brodmann areas

Article


Saha, Sajib, Nesterets, Yakov, Rana, Rajib, Tahtali, Murat, de Hoog, Frank and Gureyev, Timur. 2017. "EEG source localization using a sparsity prior based on Brodmann areas." International Journal of Imaging Systems and Technology. 27 (4), pp. 333-344. https://doi.org/10.1002/ima.22236
Article Title

EEG source localization using a sparsity prior based on Brodmann areas

ERA Journal ID36561
Article CategoryArticle
AuthorsSaha, Sajib (Author), Nesterets, Yakov (Author), Rana, Rajib (Author), Tahtali, Murat (Author), de Hoog, Frank (Author) and Gureyev, Timur (Author)
Journal TitleInternational Journal of Imaging Systems and Technology
Journal Citation27 (4), pp. 333-344
Number of Pages12
Year2017
PublisherJohn Wiley & Sons
Place of PublicationUnited States
ISSN0899-9457
1098-1098
Digital Object Identifier (DOI)https://doi.org/10.1002/ima.22236
Web Address (URL)https://onlinelibrary.wiley.com/doi/10.1002/ima.22236
Abstract

Localizing the sources of electrical activity in the brain from electroencephalographic (EEG) data is an important tool for noninvasive study of brain dynamics. Generally, the source localization process involves a high‐dimensional inverse problem that has an infinite number of solutions and thus requires additional constraints to be considered to have a unique solution. In this article, we propose a novel method for EEG source localization. The proposed method is based on dividing the cerebral cortex of the brain into a finite number of “functional zones” which correspond to unitary functional areas in the brain. To specify the sparsity profile of human brain activity more concisely, the proposed approach considers grouping of the electrical current dipoles inside each of the functional zones. In this article, we investigate the use of Brodmann's areas as the functional zones while sparse Bayesian learning is used to perform sparse approximation. Numerical experiments are conducted on a realistic head model obtained from segmentation of MRI images of the head and includes four major compartments namely scalp, skull, cerebrospinal fluid (CSF), and brain with relative conductivity values. Three different electrode setups are tested in the numerical experiments. The results demonstrate that the proposed approach is quite promising in solving the EEG source localization problem. In a noiseless environment with 71 electrodes, the proposed method was found to accurately locate up to 6 simultaneously active sources with accuracy >70%.

KeywordsBrodmann map; electroencephalography; inverse problem; source localization; sparse reconstruction
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020461199. Machine learning not elsewhere classified
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Byline AffiliationsCommonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
Institute for Resilient Regions
University of New South Wales
Institution of OriginUniversity of Southern Queensland
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